Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies.
Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies.
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DOI：
10.1016/j.kint.2021.09.028
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发表时间：
2022-02
影响因子：
19.6
通讯作者：
Zhang W
中科院分区：
文献类型：
--
作者：
Yi Z;Salem F;Menon MC;Keung K;Xi C;Hultin S;Haroon Al Rasheed MR;Li L;Su F;Sun Z;Wei C;Huang W;Fredericks S;Lin Q;Banu K;Wong G;Rogers NM;Farouk S;Cravedi P;Shingde M;Smith RN;Rosales IA;O'Connell PJ;Colvin RB;Murphy B;Zhang W
间质纤维化、肾小管萎缩和炎症是同种异体肾移植失败的主要原因。在这里，我们寻求对这些病变进行客观、定量的病理学评估，以提高预测效用，并构建了一个基于深度学习的流程，识别正常与异常的肾组织区室和单核白细胞浸润。移植活检的高碘酸希夫染色玻片（60 次训练和 33 次测试）用于量化间质、肾小管和单核白细胞浸润特异的病理损伤。该流程应用于来自移植受者的两个独立队列（GoCAR：404 名患者，AUSCAD：212 名患者）的 789 例移植活检（478 例基线[植入前]和 311 例移植后 12 个月方案活检）的整个幻灯片图像，以将复合病变特征与移植物丢失相关联。我们的模型准确地识别了肾组织区室和单核白细胞。数字特征与修订后的 Banff 2007 分数显着相关，但对低于 Banff 分数阈值的细微病理变化更敏感。基线样本中的间质和肾小管异常评分 (ITAS) 高度预测一年的移植物丢失，而移植后 12 个月方案活检中的综合损伤评分则预测以后的移植物丢失。 ITAS 和综合损伤评分优于班夫评分或临床预测因子，具有卓越的移植物损失预测准确性。根据 ITAS 或综合损伤评分分层的高/中风险群体也表现出估计肾小球滤过率下降和随后的移植物损伤的发生率显着较高。因此，我们的深度学习方法可以准确地检测和量化基线或移植后活检的病理病变，并表现出预测移植后移植物丢失的卓越能力，具有作为预防、风险分层或监测工具的潜在应用。
Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning–based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte infiltrates. Periodic acid– Schiff stained slides of transplant biopsies (60 training and 33 testing) were used to quantify pathological lesions specific for interstitium, tubules and mononuclear leukocyte infiltration. The pipeline was applied to the whole slide images from 789 transplant biopsies (478 baseline [preimplantation] and 311 post-transplant 12-month protocol biopsies) in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. Our model accurately recognized kidney tissue compartments and mononuclear leukocytes. The digital features significantly correlated with revised Banff 2007 scores but were more sensitive to subtle pathological changes below the thresholds in the Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of one-year graft loss, while a Composite Damage Score in 12-month post-transplant protocol biopsies predicted later graft loss. ITASs and Composite Damage Scores outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITASs or Composite Damage Scores also demonstrated significantly higher incidence of estimated glomerular filtration rate decline and subsequent graft damage. Thus, our deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies and demonstrated superior ability for prediction of post-transplant graft loss with potential application as a prevention, risk stratification or monitoring tool.
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